demo.git/cnn_v3/tools, branch main

update weights

2026-03-29T14:51:22Z

update assets and weights

2026-03-29T14:39:53Z

fix(audio): fix early timing drift in tracker, use ola_decode_frame in synth

2026-03-28T19:14:59Z

- Replaced chunk_frames truncation accumulation with accurate double-precision integration in audio_render_ahead. - Updated tracker to use double-precision time representations for exact sample-accurate scheduling. - Extracted ola_decode_frame to handle per-frame OLA-IDCT synthesis in synth.cc. - Updated TODO.md for completed audio tasks. handoff(Claude): Audio timing drift and OLA-IDCT enhancement resolved.

fix(cnn_v3): remove dec0 ReLU, load FiLM MLP at runtime

2026-03-27T06:59:00Z

Two bugs blocking training convergence: 1. dec0 ReLU before sigmoid constrained output to [0.5,1.0] — network could never produce dark pixels. Removed F.relu in train_cnn_v3.py and max(0,…) in cnn_v3_dec0.wgsl. Test vectors regenerated. 2. set_film_params() used hardcoded heuristics instead of the trained MLP. Added CNNv3FilmMlp struct + load_film_mlp() to cnn_v3_effect.h/.cc. MLP auto-loaded from ASSET_WEIGHTS_CNN_V3_FILM_MLP at construction; Linear(5→16)→ReLU→Linear(16→72) runs CPU-side each frame. 36/36 tests pass. Parity max_err=4.88e-4 unchanged. handoff(Gemini): retrain from scratch — needs ≥50 samples (currently 11). See cnn_v3/docs/HOWTO.md §2-3.

update the weights

2026-03-26T07:13:43Z

feat(cnn_v3): upgrade architecture to enc_channels=[8,16]

2026-03-26T06:03:01Z

Double encoder capacity: enc0 4→8ch, enc1 8→16ch, bottleneck 16→16ch, dec1 32→8ch, dec0 16→4ch. Total weights 2476→7828 f16 (~15.3 KB). FiLM MLP output 40→72 params (L1: 16×40→16×72). 16-ch textures split into _lo/_hi rgba32uint pairs (enc1, bottleneck). enc0 and dec1 textures changed from rgba16float to rgba32uint (8ch). GBUF_RGBA32UINT node gains CopySrc for parity test readback. - WGSL shaders: all 5 passes rewritten for new channel counts - C++ CNNv3Effect: new weight offsets/sizes, 8ch uniform structs - Web tool (shaders.js + tester.js): matching texture formats and bindings - Parity test: readback_rgba32uint_8ch helper, updated vector counts - Training scripts: default enc_channels=[8,16], updated docstrings - Docs + architecture PNG regenerated handoff(Gemini): CNN v3 [8,16] upgrade complete. All code, tests, web tool, training scripts, and docs updated. Next: run training pass.

fix(cnn_v3/tools): rename Output→Dec0 in viz panel; fix BN weight cnt 72→584

2026-03-25T18:14:28Z

- Layer viz button was labeled 'Output' instead of 'Dec0' - BN parseWeights cnt was stale (old 1×1 conv size); now 8×8×9+8=584 handoff(Gemini): web tool only, no C++ or shader changes

feat(cnn_v3): 3×3 dilated bottleneck + Sobel loss + FiLM warmup + architecture PNG

2026-03-25T09:05:42Z

- Replace 1×1 pointwise bottleneck with Conv(8→8, 3×3, dilation=2): effective RF grows from ~13px to ~29px at ¼res (~+1 KB weights) - Add Sobel edge loss in training (--edge-loss-weight, default 0.1) - Add FiLM 2-phase training: freeze MLP for warmup epochs then unfreeze at lr×0.1 (--film-warmup-epochs, default 50) - Update weight layout: BN 72→584 f16, total 1964→2476 f16 (4952 B) - Cascade offsets in C++ effect, JS tool, export/gen_test_vectors scripts - Regenerate test_vectors.h (1238 u32); parity max_err=9.77e-04 - Generate dark-theme U-Net+FiLM architecture PNG (gen_architecture_png.py) - Replace ASCII art in CNN_V3.md and HOW_TO_CNN.md with PNG embed handoff(Gemini): bottleneck dilation + Sobel loss + FiLM warmup landed. Next: run first real training pass (see cnn_v3/docs/HOWTO.md §3).

update weights

2026-03-25T07:54:30Z

feat(cnn_v3/tools): embed default weights in HTML tool; add --html export flag

2026-03-25T05:25:53Z

- cnn_v3/tools/weights.js: new file — base64-encoded cnn_v3_weights.bin + cnn_v3_film_mlp.bin; loaded at startup so the tool works without dropping files - tester.js: preload() falls back to embedded weights.js constants when fetch fails; logs "Loaded embedded" vs "Preloaded" to distinguish the two paths - index.html: load weights.js before tester.js - export_cnn_v3_weights.py: add --html / --html-output flags that call update_weights_js() to regenerate weights.js after a training run - HOW_TO_CNN.md: update pipeline diagram, §3 export commands, §7 HTML tool section (file table, workflow, weights.js description), Appendix A handoff(Gemini): weights.js now the canonical source for HTML tool defaults; regenerate with `uv run export_cnn_v3_weights.py --output ... --html`